Abstract
The paper presents a study on health diagnosis and prognosis of an industrial diesel motor. Two well-known approaches, Hidden Markov Model (HMM) and particle filter (PF), are applied from real recorded data with different measurements. The recorded data is firstly pre-processed and health indicator is then chosen before implementing each used approach. The obtained results are analyzed and discussed. The use and advantages of each approach are finally highlighted.
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Industrial enterprise specialized in the development of solutions monitoring, diagnosis and prediction of failure for industrial facilities. Website: www.predict.fr/.
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Acknowledgement
Special thanks to Predict for the collaboration through this project, National Research Agency, the University of Lorraine, the National Center for Scientific Research for supporting and financing PHM factory, our joint laboratory.
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Mechri, W., Vu, HC., Do, P., Klingelschmidt, T., Peysson, F., Theilliol, D. (2018). A Study on Health Diagnosis and Prognosis of an Industrial Diesel Motor: Hidden Markov Models and Particle Filter Approach. In: Kościelny, J., Syfert, M., Sztyber, A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-319-64474-5_32
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DOI: https://doi.org/10.1007/978-3-319-64474-5_32
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